The majority of commercial range-imaging systems are generally related to two main groups: a) triangulation systems; and b) TOF (Time of Flight) systems. Other less common prior art systems and methods thereof include a Shape-from-Shading technique (as presented in the article “Shape from shading: a method for obtaining the shape of a smooth opaque object from one view” by B. K. P Horn, Technical Report 79, Project MAC, Massachusetts Institute of Technology (MIT), 1970), a Shape-from-Defocus technique (as presented in the article “A simple, real-time range camera” by A. Pentland et. al, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 1989), a Photometric-stereo technique (as presented in the article “Photometric method for determining surface orientation from multiple images” by R. J. Woodham, Optical Engineering, Volume 19, No. 1, Pages 139-144, January-February 1980), a Moire-Interferometry technique (as presented in the article “Moire topography” by H. Takasaki, Applied Optics, Volume 9, Issue 6, Pages 1467-1472, 1970), and others.
According to the prior art, systems from the above triangulation group are essentially based on a fundamental quality of light—its travel in straight lines. This geometrical attribute constitutes the difference between what is seen with the right and left eye, which is what human binocular depth perception (Stereopsis) is based on. In turn, the triangulation group consists of two major sub-groups: Passive-Triangulation and Active-Triangulation.
Inspired by various biological systems, the Passive-Triangulation methods (also known as the Passive Stereo methods) are probably the oldest methods for range imaging, where depth data is extracted through matching correspondences between two or more images taken from different angles. Despite nearly fifty years of research in the field (such as taught by B. Julesz in the article titled “Towards the automation of binocular depth perception (AUTOMAP-1)”, Proceedings of IFIPS Congress, Munich, Pages 439-444, 1962), conventional Passive-Triangulation methods suffer from significant inabilities to recapture featureless areas, areas with repeating patterns and structures, and also surface boundaries. Despite these shortcomings, Passive-Triangulation is still appealing for certain tasks aimed at reconstructing highly textured and relatively continuous areas, such as 3-D modeling from high altitude aerial photographs (e.g., as presented in the article by L. H. Quam et. al., “Stanford automatic photogrammetry research”, Technical Report STAN-CS-74-472, Department of Computer Science, Stanford University, December 1974).
In contrast to Passive-Triangulation systems, Active-Triangulation systems are known in the art for their high reliability. In these systems, the “notorious” correspondence problem (such as described for example in the following Web page http://en.wikipedia.org/wiki/Correspondence_problem) is completely avoided due to use of an additional dedicated active illumination source, which projects Structured Light over the acquired scene. Structured Light has a designated layout whose purpose is to provide a distinct identification to each local region of it that is back-reflected from the scene and caught by a camera. Since the correspondence takes place according to a predefined format, there is no need for two cameras, and only one camera and one projector can usually be used for performing the required triangulation. Besides being reliable and having a relatively simple design, these Active-Triangulation systems also have relatively high accuracy in depth measurement.
However, unlike Passive-Triangulation systems, the majority of Active-Triangulation systems are usually limited to range imaging of static environments (objects). This is because they actually utilize a temporal Structured Light format, meaning the use of a synchronized sequence of projections and image captures, starting with the “classic” Light-Stripe (such as presented by Y. Shirai et. al. in the article titled “Recognition of polyhedrons with a range finder”, 2nd International Joint Conference on Artificial Intelligence (IJCAI), Pages 80-87, London, 1971) that scans the imaged environment in a controlled angular motion, and ending, for example, with more advanced systems which utilize a video projector to rapidly exchange various Coded-Light patterns (such as disclosed in U.S. Pat. No. 4,175,862). In addition, it should be noted that in conventional Active-Triangulation systems the acquired scene also must remain static until the scanning process or pattern switching is accomplished.
On the other hand, if only a single Coded-Light pattern is used, it is possible to acquire the scene in motion as well. This concept has been a subject to research since the early days of the Active-Triangulation approach (such as presented by P. M. Will et. al. in the article titled “Grid coding: A preprocessing technique for robot and machine vision”, Computers and Artificial Intelligence, pages 66-70, 1971). The main challenge in developing such types of codification methods usually involves embedding sufficiently large identification code words within small local regions of a single pattern (as presented in the article titled “Pattern codification strategies in structured light systems” by J. Salvi, Pattern Recognition, Volume 37, Issue 4, Pages 827-849, April 2004). Most of the prior art approaches include the encoding of colored stripes (such as presented by K. L. Boyer et. al. in the article titled “Color-encoded structured light for rapid active ranging”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 9, Issue 1, Pages 14-28, January 1987) or colored spots (as presented by R. A. Morano in the article titled “Structured light using pseudorandom codes”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 20, Issue 3, Pages 322-327, March 1998). These approaches are mainly suitable for capturing neutral colored objects under relatively low ambient light conditions. Other prior art approaches suggest using various shapes and spatial features as an alternative to the color (as presented by P. Vuylsteke et. al. in the article titled “Range image acquisition with a single binary-encoded light pattern”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Volume 12, Issue 2, Pages 148-163, 1990).
According to the prior art, Active-Triangulation systems require setting a camera and projector apart by a predefined distance, known as the Stereo-Baseline. Usually, the magnitude of the baseline is set to approximately 10% to 50% of the distance to the targeted scene. In general, a shorter baseline may result in low accuracy levels, while a larger baseline may result in difficulties to capture surfaces that are not facing the system. This camera/projector separation requirement is usually the cause of most of the apparent shortcomings of the conventional Active-Triangulation systems, which generally are:                the known “stereo occlusion problem”: a surface may be reconstructed only if it maintains a clear line of sight with both the camera and projector. This condition cannot be met when dealing, for example, with deep concavities.        applicability to short ranges only: since the baseline is set proportionally to the measurement distance, long ranges require impractical baseline lengths. For example, to measure objects a hundred meters away at high precision would require a baseline length of at least ten meters. Since the triangulation requires the camera and projector to maintain their relative position in a relatively precise manner, this would usually require a bulky and sturdy design and, possibly, also a controlled environment, isolated from both vibrations and temperature changes.        
The common choice for long-distance range-imaging is the TOF (Time-of-Flight) technology. Unlike Active-Triangulation systems, conventional TOF systems are based on another fundamental quality of light—its travel at a constant speed. Systems from the above-mentioned TOF group actually serve as Optical-Radars, measuring the time it takes for rapid light pulses to return from the targeted scene, using a fast timing circuitry. Most of the TOF systems operate by scanning point by point, and are therefore only suitable for static scenes. Such 3-D scanners are the terrestrial LIDAR (Light Detection and Ranging) systems. However, some TOF systems, such as the conventional Flash-LIDAR systems (presented for example, by R. Lange et. al. in the article titled “Time-of-flight range imaging with a custom solid-state image sensor”, Laser Metrology and Inspection, Proceedings of the SPIE, Volume 3823, Pages 180-191, Munich, September 1999), have the capability to simultaneously acquire a multiplicity of points, making them also suitable for motion range-imaging. Further, TOF technology differs from Active-Triangulation approach by another important feature: it does not require a baseline. Thus, by using conventional prisms, it is possible to set both the camera and the pulsed light emitter in an SOP (Single-Optical-Path) configuration. Since both imaging and light emission are carried over the same optical path, no “stereo occlusions” occur and the system can operate at large distances regardless of physical size. In practice, a “nearly” SOP configuration, where the camera and the light emitter are set close to each other, is often implemented, since it simplifies the system while introducing, at the most, only negligible “stereo occlusions”.
However, while TOF technology provides a solution for both range-imaging of distant scenes as well as concave surfaces, TOF systems are usually more complicated, and thus more costly than Active-Triangulation systems. Furthermore, state of the art motion-capable range-imaging TOF technologies are usually considered inferior to Active-Triangulation systems, both in terms of range measurement accuracies as well as in terms of lateral resolution.
Another prior art SOP (Single Optical Path) range-imaging method, which also does not require a Stereo-Baseline, is introduced by D. Sazbon et. al. in the article titled “Qualitative real-time range extraction for preplanned scene partitioning using laser beam coding”, Pattern Recognition Letters, Volume 26, Issue 11, Pages 1772-1781, August 2005. This method is based on the projection of a multiplicity of axially varied illumination patterns generated by a beam-shaping optical element. In the original experimental design, each pattern consisted of an array of slits oriented at a specific angle. Thus, the distance to a certain surface was indicated by the orientation of the reflected slits, captured by a camera coupled to a projector. Similarly to conventional TOF systems, this system can be set in an SOP configuration, where both the projector and the acquisition camera share (or nearly share) the same optical axis, and where the range measurement accuracy does not depend on their distance. Yet unlike TOF systems, it benefits from a simple design, since it requires no moving parts (e.g., no scanning mirrors), no fast timing circuitry, and no significantly sensitive high form-factor pixel architecture (thus, allowing the use of common low-cost high resolution camera sensors). Also, similarly to Single-Pattern Active-Triangulation methods, this range-imaging method allows motion range-imaging of dynamic scenes, since it requires only a single camera snapshot to acquire a complete range-image. However, in the above range-imaging method proposed by D. Sazbon et al., the measurement accuracy, as well as the acquisition range, depends upon the number of discrete patterns that the element can generate. The design of the element may be performed by using a conventional iterative numerical approach (such as presented by U. Levy et. al. in the article titled “Iterative algorithm for determining optimal beam profiles in a three-dimensional space”, Applied Optics, Volume 38, Issue 32, Pages 6732-6736, 1999, which is further based on the article of R. W. Gerchberg et. al. titled “A practical algorithm for the determination of phase from image and diffraction plane pictures”, Optics, Volume 35, Pages 237-246. 1972). According to the iterative numerical approach, a set of desired light intensity distributions (i.e., patterns) are imposed for a number of discrete planes positioned at varied axial ranges. However, there is a non-linear transition between the intensity images created at different planes (limiting measurement accuracy for the 3-D and range estimation), which also means that the measured workspace is partitioned by a set of sparse discrete range segments. This characteristic is not problematic in applications, such as robotic navigation and obstacle detection, where spatiotemporally sparse range measurements may suffice. For example, it should be sufficient that the robot would be able to distinguish between a set of predefined ranges. However, in applications such as 3D modeling for content creation, product quality control in manufacturing, medical imaging and others, much higher measurement densities are usually required.
Thus, there is a continuous need in the art to provide a relatively accurate SOP three-dimensional and range imaging method and system thereof, which eliminates the traditional requirement for a Stereo-Baseline, and allows containing optical elements within a relatively small package, regardless of the measurement distance, while substantially avoiding the known “stereo occlusion problem”.
Also, there is a continuous need in the art to provide a method and system for 3-D object profile and range inter-planar estimation, such a system having relatively simple and sturdy design and containing relatively few elements with no moving parts, and also substantially eliminating the need for user calibration.
In addition, there is a need in the art to provide a method and system for 3-D and range estimation enabling using imaging device sensors that are relatively inexpensive, have relatively low sensitivity and relatively high resolution.